The Four V of Big-Data
When we refer to Big-Data, we do not consider databases with significant Volume of data, but something more complex.
Big-Data is not only about Volume, but is also about Velocity, Variety and Veracity. All together they make the Four V of Big-Data.
Big-Data is generated at a high-velocity. Think about the speed at which we produce data just by browsing Facebook, Thousands of records in minutes.
Another critical characteristic is the Variety of data. Big-Data comes in various forms, text, images and sound. Large Volumes and Variety is what makes Big-Data quality and readability quite uncertain. Here it comes the necessity to check their Veracity and distil them as we do for oil to transform it into gasoline.
Did you know?
The amount of data we produce every day is increasing exponentially. Nowadays, almost everything we do leaves a digital trace.
And we are not just talking about structured data organized into tables. Today, unstructured data such as videos, pictures, text etc. make up 80% of the world's data.
This gives companies access to unprecedented amounts of data they can use to get useful insights. From logistics to marketing and customer service, no business unit won't be transformed by data.
If Artificial Intelligence is a car ...
Machine Learning, Artificial Intelligence, Big Data… We hear these words non-stop nowadays, but the confusion around these topics and the connections between them remains. We want to shed some light on these concepts and, to do this, we are going to use … a car. I know what you are thinking: “Wait! Weren’t we talking about Artificial Intelligence?!” Don’t worry, you’ll understand in a second. For now, just trust us. Think of Artificial Intelligence as a car, fasten your seatbelts and let’s get started!
… Big Data is the oil
It was 2017 when the Economist published a popular article stating that Data is the new oil. The article was referring to the value of data in today’s digital economy, and facts are proving its observations to be right. However, when we talk about Artificial Intelligence, the same metaphor holds. Data is the new oil in the sense that modern Artificial Intelligence applications need data to work as much as a car needs fuel to move. Careful though, oil alone is not enough!
… Smart Data is the fuel
If you put oil in your car’s gas tank, consequences won’t be pleasant. That’s why when we go to a gas station they will sell us gas and not oil. And gas is, simply put, a “purified” version of oil. And there is more, the higher the quality of the gas, the higher your car’s performance will be. Same story for Data. If you want to get useful results from your Artificial Intelligence applications, Big Data is not enough. Smart Data is what you need and Smart Data is what you get when you clean, filter and transform Big Data. (See “Big Data: Using SMART Big Data, Analytics and Metrics To Make Better Decisions and Improve Performance” by Bernard Marr for more on Smart Big Data)
… and Machine Learning is the engine
Any car needs its engine, and here is where Machine Learning comes into play. Nowadays, Machine Learning algorithms are the beating heart of Artificial Intelligence, as much as the engine for a car. And, just like an engine, they need fuel (Data) to work. Machine Learning is a family of algorithms that derives from different branches of statistics and applied mathematics. Machine Learning algorithms learn from data, the more (Smart) data they have to learn, the better and more accurate the results will be.
Finally, a car!
Oil, refineries and engines wouldn’t be so crucial if we didn’t use them for something. In our metaphoric game, we have picked a car as the final use of natural resources and technology. By the same token, Big-Data, Smart-Data and Machine Learning wouldn’t be so useful without their support to the application of Artificial Intelligence.
This doesn’t mean that the fuel is useless alone. Even though cars (both electric or internal combustion ones) cannot merely exist without electricity or gasoline, you can still do pretty useful things with electricity and gasoline alone (e.g. heating). Same for data and, for instance, its applications in business intelligence.
...but an intelligent one.
If you followed us till now, you would feel disappointed if we didn’t make the last mile, which is the intelligent component. So what makes this car (this application) intelligent? Simplifying a bit, we talk about Artificial Intelligence any time a machine does something that we perceive as intelligent or that would have required human intuition to be carried out. So that’s why we pushed our parallel to the intelligent car. It surprises us. It can drive (maybe only for a few seconds for the time being) without our intervention and can alert us if something is going wrong and take action. The same holds for Artificial intelligence; it is meant to improve our productivity, our life experience, and not to substitute us.
How to prepare your company for A.I.
If you have followed us through this “explain like I’m 5” game, now you understand why it’s essential for companies to prepare for A.I. applications. Artificial intelligence doesn’t just happen. It needs Data (the oil) to run. These data have to be cleaned and refined to turn into Smart Data (the gas). Smart Data is fed to Machine Learning algorithms (the engine) to make it work. And only after all the components are in place, the car will move.
The subtle difference between an intelligent car and A.I. application is that the fuel needed for the vehicle is, in most cases, something that you cannot buy at the gas station. It is instead something that you need to source and drill internally to the company. That is why it is so important for companies to invest in data storage, validation and cleaning. If you are not investing now in building your oil reservoir, you will have tough times in applying A.I. in your company.
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Over the last weeks, COVID-19 has monopolized the attention of mass media. Indeed, the consequences of the pandemic are unprecedented and heavily impact all spheres of our lives.
Leveraging Big Data Analytics we are mapping the discussion surrounding the pandemic in traditional news media (i.e online editions and videos published by major news sources) in Italy, UK, US and Canada. The new feature of our News Tracker allows users to explore the evolution of interest in different news topics.
As an example, the topic “Boris Johnson” (UK Prime Minister) breaks into the debate significantly around the 16th of March, after that UK adopted the “herd immunity” strategy against COVID-19, causing mixed reactions.
In a connected world, where news run at the speed of light in fibre optics, this approach sheds light on where the attention of people was, is, and is going, and offers additional insights to interpret the behaviour of people in such turbulent times.
We are honoured to collaborate with researchers from Greenwich University and ISI Foundation for this project on tracking and mapping news on COVID-19.
What is the project about?
COVID-19 News Tracker is a tool aimed at mapping the discussion surrounding the COVID-19 pandemic in traditional news media. The goal is to help users exploring the latest development considering a multitude of sources.
For the analysis, we consider Articles and Youtube Videos published by major traditional news media organizations in Italy, United Kingdom and United States.
Currently, the tracker supports the following features:
How can I access the results?
The results of the research are openly available on our data-automation and Machine Learning platform Scops. Visit covid19.scops.ai to explore them!
Did you know?
Just over the last two years, organisations that have deployed Artificial Intelligence grew from 4% to 14% according to Gartner.
From optimising inventories, supplies, liquidity levels and customer demand, predicting future scenarios allows companies to plan their business activity better.
Our SaaS platform Scops runs Machine Learning models that allow companies to create scenario analysis based on past and present data giving you the probability that a particular scenario occurs.
Did you know?
Data preparation eats-up to 80% of Data-Scientists' time! It means that most of the resources are not invested in making Data-Driven decisions but in preparing Data.
Our Saas platform Scops automates the data-preparation, letting you focus on the analysis and making Data-Driven decisions faster.
Did you know that?
"AI could contribute up to $15.7 trillion to the global economy by 2030, more than the current output of China and India combined.
Of this, $6.6 trillion is likely to come from increased productivity and $9.1 trillion is likely to come from consumption-side effects."1
Quick-Algorithm is committed in helping companies to become more Data-Driven with our Machine-Learning technology.